Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
Transformers.js
SegformerForSemanticSegmentation
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Pytorch
vision
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custom_code
Instructions to use IsGarrido/RMBG-1.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IsGarrido/RMBG-1.4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="IsGarrido/RMBG-1.4", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("IsGarrido/RMBG-1.4", trust_remote_code=True, device_map="auto") - Transformers.js
How to use IsGarrido/RMBG-1.4 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'IsGarrido/RMBG-1.4'); - Notebooks
- Google Colab
- Kaggle
Download requirements.txt from IsGarrido/RMBG-1.4: direct link, hf CLI and curl.
- Browser
- Download file 87 Bytes
-
https://huggingface.co/IsGarrido/RMBG-1.4/resolve/main/requirements.txt
- Command line
-
hf download hf://IsGarrido/RMBG-1.4/requirements.txt
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curl -L -o requirements.txt https://huggingface.co/IsGarrido/RMBG-1.4/resolve/main/requirements.txt
87 Bytes
| torch | |
| torchvision | |
| pillow | |
| numpy | |
| typing | |
| scikit-image | |
| huggingface_hub | |
| transformers>=4.39.1 |